"when CMB photons inverse-Compton scattered by hot plasma in a galaxy cluster's potential well"
-Shimwell et al. (2013)
"blind galaxy cluster survey [...] aiming to detect via the Sunyaev-Zel'dovich effect"
-page 1
#### Large array

J0300+2613 via SZJ0300+2613 is just galactic dust! (not SZ, but AME)GLEAM 200 MHz dataGB6 5 GHz data

J0300+2613 correlates with IR emissionJ0300+2613 look like in MIR data?J0300+2613 is really Galactic emission-Is it necessarily AME?#Load `J0300+2613` coordinates into python:
from astropy.coordinates import SkyCoord # Low-level frames
import astropy.units as u
coord = SkyCoord('3:00:08.9','+26:16:29.1',frame='icrs', unit=(u.hourangle, u.deg))
#Convert to degrees RA DEC:
coord
#So the kernel doesn't crash when we plot stuff in a notebook
%matplotlib inline
# Convenient package for plotting FITS images
import aplpy
def fits_display(image_file):
# Create a new figure
fig = aplpy.FITSFigure(image_file)
# Show the colorscale
fig.show_colorscale()
# Make ticks white
fig.ticks.set_color('white')
# Make labels smaller
fig.tick_labels.set_font(size=15)
# Round out those tick labels:
fig.tick_labels.set_xformat('ddd.dd')
fig.tick_labels.set_yformat('ddd.dd')
# Overlay a grid
fig.add_grid()
fig.grid.set_alpha(0.9)
# Add a colorbar
fig.add_colorbar()
fig.colorbar.set_axis_label_text('Intensity (MJy/sr)')
return fig
Zodi is not subtracted!
# 9 micron data
fits_display('/home/aaronb/J003+2613/image/J003+2613_S.fits')
# 18 micron data
fits_display('/home/aaronb/J003+2613/image/J003+2613_L.fits')
# How the region looks in Zodi-subtracted All-sky Tiles
# 9 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G156m27/G156m27_S_intensity.fits')
# 18 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G156m27/G156m27_L_intensity.fits')
# 9 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G157m30/G157m30_S_intensity.fits')
# 18 micron data:
fits_display('/work2/users/onaka/AKARI/IRC_AllSkyMap_2016/G157m30/G157m30_L_intensity.fits')